Regenerative Braking Route Control for EV Energy Recovery
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Solution Overview
Problem
Conventional regenerative braking systems in electrified vehicles are limited in determining when to apply regenerative braking, primarily relying on driving style and vehicle speed, leading to suboptimal energy usage and drivability.
Innovation Solution
A dynamic regenerative braking system that utilizes GPS/map data, ADAS sensors, and real-time road and environmental data to determine an energy-efficient navigational route and adjust regenerative braking levels dynamically based on terrain, traffic, and road conditions to maximize power regeneration and drivability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If conventional regenerative braking systems activate based on driving style and vehicle speed, then the system structure remains simple, but energy efficiency and power regeneration are suboptimal
Solution Approach 1:
The system performs preliminary analysis of GPS route data, map information, and weather forecasts before the trip to pre-determine optimal regenerative braking opportunities. This allows the system to prepare and activate regenerative braking in advance at optimal moments, maximizing energy recovery without requiring complex real-time decision-making during driving.
Solution Approach 2:
The system continuously monitors actual vehicle operation data, compares it with the pre-planned optimal strategy, and dynamically adjusts regenerative braking activation. This feedback mechanism enables the system to adapt to real-time conditions while maintaining optimal energy efficiency, resolving the contradiction between simple structure and high performance.
2Productivity
If regenerative braking is activated based on separate power maps driven by vehicle speed and driver power demands, then the control logic remains straightforward, but the system cannot determine optimal regenerative braking timing
Solution Approach 1:
The control system integrates multiple functions into a single unified controller that processes GPS data, map information, weather forecasts, and vehicle operation data simultaneously. This multi-functional controller determines optimal regenerative braking timing by considering route topography, traffic conditions, and environmental factors, achieving high power regeneration without proportionally increasing system complexity.
Solution Approach 2:
The system pre-analyzes the entire route using GPS and map data to identify upcoming downhill sections, traffic lights, and congestion areas where regenerative braking would be beneficial. This preliminary route analysis enables the system to proactively activate regenerative braking at optimal moments rather than reacting to immediate vehicle conditions alone.
3Use of energy by moving object
If the system continuously updates regenerative braking levels based on real-time GPS, traffic, and road grade data, then energy efficiency is maximized, but computational requirements and processing complexity increase
Solution Approach 1:
The system pre-processes GPS route data and map information to create a structured representation of upcoming regenerative opportunities, such as downhill sections and traffic congestion areas. This pre-processing reduces the computational burden during real-time operation by organizing data in advance, allowing continuous optimization without excessive processing complexity.
Solution Approach 2:
The control system automatically integrates and processes multiple data sources (GPS, map data, weather forecasts, vehicle sensors) without requiring external intervention. The system self-manages the complex data fusion and decision-making processes, continuously adjusting regenerative braking levels based on real-time conditions while maintaining energy efficiency optimization.
4Ease of operation
If regenerative braking levels are adjusted based on multiple factors including traffic congestion and road grade, then drivability and energy efficiency improve, but the control algorithm complexity increases
Solution Approach 1:
The system dynamically adjusts regenerative braking levels by changing key parameters such as braking torque, activation timing, and power distribution based on multiple factors including road grade, traffic conditions, and battery state of charge. These parameter adjustments are made according to pre-established optimization criteria derived from route analysis, improving drivability and energy efficiency without requiring overly complex control algorithms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances energy efficiency by optimizing regenerative braking based on real-time data, improving vehicle range and drivability by intelligently managing energy storage and adjusting braking levels to match driving conditions.
Implementation Method 1
an electric motor configured for regenerative braking that is separate from friction brakes
Data Source
AI summary
A dynamic regenerative braking system (DRBS) for an electrified vehicle having a high voltage (HV) battery system includes a user interface configured to display information and to receive user input for a route selection of the electrified vehicle, a global positioning satellite (GPS)/map data system configured to obtain GPS and map data associated with a current trip of the electrified vehicle, an advanced driver assistance system (ADAS) module having one or more sensors configured to obtain surrounding vehicle data and upcoming road conditions data, and a brake module configured to control a regenerative braking system and provide current road grade data. A controller is programmed to determine an energy efficient navigational route to a destination, which maximizes a power regeneration of the HV battery system via the regenerative braking system, based on the GPS/map data, the surrounding vehicle data, the upcoming road conditions data, and the current road grade data.